Shoaib Khanmohammadi

In the current research, an innovative biomass-based energy system is proposed for power and desalinated water production. The plant's primary components consist of a gasifier, a compressor, a heat exchanger, a gas turbine, a combustion chamber, and a Multi-effect desalination with thermal vapor compression (MED-TVC) unit. A comprehensive thermodynamic and thermoeconomic assessment is conducted on the suggested plant. Besides, a parametric evaluation is conducted to determine the effect of primary decision variables on the system performance. Multiple objective optimization using the multi-objective grey wolf optimizer (MOGWO) algorithm is carried out to obtain the optimal solution with the highest exergy efficiency and the minimum amount of total cost rate. The artificial neural network (ANN) has an intermediary role in the optimization process to decrease computational time and enhance optimization speed. The relation between the objective function and decision variables is investigated, employing ANN to determine the energy system's optimum point. The generation rate for power and freshwater at the optimal point is equal to 5127 kW and 38.6 kg/s, respectively. Besides, the optimum value of the exergy efficiency and total cost rate are computed as 15.61% and 206.78 $/h, respectively. The results also revealed that the number of effects of the desalination unit does not affect the carbon dioxide emissions. Moreover, the scatter distribution of the key decision variable indicates that the air compressor pressure ratio is not a sensible variable, and their optimum points are distributed across the entire domain.